{"id":"W104645259","doi":"10.1007/978-3-642-23094-3_11","title":"Interactive Segmentation with Super-Labels","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Pixel; Object (grammar); Histogram; Image segmentation; Coherence (philosophical gambling strategy); Computer vision; Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005052438,0.0001653434,0.0001469753,0.0003528021,0.0001181592,0.0002143262,0.001447163,0.00004295259,0.00004178042],"category_scores_gemma":[0.00007823219,0.0001261343,0.00002191013,0.001609979,0.0004074436,0.001919113,0.0003715909,0.000227885,0.00002067152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001347089,"about_ca_system_score_gemma":0.0001640536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008117316,"about_ca_topic_score_gemma":0.00003098217,"domain_scores_codex":[0.9980854,0.0000738466,0.0002298432,0.0006604941,0.0005813839,0.0003690515],"domain_scores_gemma":[0.9989104,0.0001654498,0.00009283883,0.0005365082,0.0001658867,0.0001288639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001066393,0.0001084289,0.002297237,0.000007447749,0.000004738646,0.00004999869,0.008725988,0.0002423535,0.01091354,0.0002600959,0.00001017216,0.9773693],"study_design_scores_gemma":[0.0003785558,0.0004208496,0.004316232,0.00007345561,0.00000244311,0.0000587795,0.000005282975,0.1184876,0.8684213,0.007572984,0.000003323076,0.0002592229],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01331399,0.00001543391,0.9855129,0.0002277597,0.0003266914,0.0002613076,4.208535e-7,0.0002327788,0.0001086887],"genre_scores_gemma":[0.4602053,0.000001896296,0.538729,0.00102229,0.00002188962,0.00001459144,5.129274e-7,0.000003832035,6.475034e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9771101,"threshold_uncertainty_score":0.5143606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040730788042411,"score_gpt":0.2787925118847826,"score_spread":0.2583852040043585,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}